Historical analysis of landscape change in the eastern boreal mixedwood: A case study in the context of cohort-based management
Bibliographic record
Abstract
Maintenance of forest spatial structure through forest management is increasingly recognized as an important landscape-level aspect to maintaining biodiversity. In this study, we use forest inventory maps dating from 1965, 1972, 1983, and 1994 to describe the recent historical spatial structure at the Lake Duparquet Research and Teaching Forest (LDRTF) in northwestern Quebec, Canada. Mapped forest stands were classified according to a conceptual cohort model of stand development based on species composition and other stand attributes. Contiguous forest stands of the same cohort class were agglomerated to form relatively uniform patches containing a single cohort class. Landscape spatial structure was defined using four landscape characterization indices: proportion of the landscape, mean patch size, mean distance to nearest neighbour and mean patch shape index. Cohort 1 patches occurred as both small and large units that had complex shapes while Cohort 2 patches occurred as small dispersed units that had simple shapes. Cohort 3 patches dominated the landscape matrix and formed both small and large units that were generally agglomerated in the same vicinity. Our cohort classification of mapped stand polygons was validated by comparing the proportion of land occupied by each cohort in two natural fires (1923 and 1760) that have shaped this area. Theoretical local targets for maintaining cohort structure in the landscape were then formulated based on these historical landscape characteristics.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".